No-reference quality index of depth images based on statistics of edge profiles for view synthesis

Abstract

Virtual view synthesis has been increasingly popular due to the wide applications of multi-view and free-viewpoint videos. In view synthesis, texture images are rendered to generate the new viewpoint with the guidance of the depth images. The quality of depth images is vital for generating high-quality synthesized views. While the impact of texture image and the rendering process on the quality of the synthesized view has been extensively studied, the quality evaluation of depth images remains largely unexplored. With this motivation, this paper presents a no-reference image quality index for depth maps by modeling the statistics of edge profiles (SEP) in a multi-scale framework. The Canny operator is first utilized to locate the edges in depth images. Then the edge profiles are constructed, based on which the first-order and second-order statistical features are extracted for portraying the distortions in depth images. Finally, the random forest is employed for building the quality assessment model for depth maps. Experiments are conducted on two annotated view synthesis image/video quality databases. The experimental results and comparisons demonstrate that the proposed metric outperforms the relevant state-of-the-art quality metrics by a large margin. Furthermore, it has better generalization ability.